Testing the Long-Memory Features in Return and Volatility of NSE Index


Long-term memory of stock markets is a topic that has not received its due attention from academics. Posting the assertion made by Fama, 1970 [1] about markets being efficient, no one can consistently outrun it for a longer duration. Handful of papers checked the efficiency in emerging markets to see if the efficiency proposition held true. Furthering the literature in this study we test for the long-term memory of National Stock Exchange (NSE) index, Nifty and NSE_500 which are a collection of 50 and 500 listed firms respectively in India. The duration of the data for study is roughly eight years over the period from 2006-06-29 to 2012-09-13, a total of 1545 observations. We observe that long-term memory does exist in the context of Indian stock market index.

Share and Cite:

Ahamed, N. , Kalita, M. and Tiwari, A. (2015) Testing the Long-Memory Features in Return and Volatility of NSE Index. Theoretical Economics Letters, 5, 431-440. doi: 10.4236/tel.2015.53050.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Fama, E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25, 383-417.
[2] Bilel, T.M. and Nadhem, S. (2009) Long Memory in Stock Returns: Evidence of G7 Stocks Markets. Research Journal of International Studies, 9, 36-46.
[3] Corhay, A., Rad, A.T. and Urbain, J.P. (1995) Long Run Behavior of Pacific Basin Stock Prices. Applied Financial Economics, 5, 11-18.
[4] Gurgul, H. and Wójtowicz, T. (2006) Long Memory on the German Stock Exchange. Czech Journal of Economics and Finance, 56, 447-468.
[5] Kang, S.H., Cheong, C.C. and Yoon, S.M. (2010) Long Memory Volatility in Chinese Stock Markets. Physica A, 389, 1425-1433.
[6] Tan, S. and Khan, M. (2010) Long-Memory Features in Return and Volatility of the Malaysian Stock. Economics Bulletin, 30, 3267-3281.
[7] Kang, S.H. and Yoon, S.M. (2007) Long Memory Properties in Return and Volatility: Evidence from the Korean Stock Market. Physica A, 385, 597-600.
[8] Kang, S.H. and Yoon, S.M. (2008) \Long Memory Features in the High Frequency Data of the Korean Stock Market. Physica A, 387, 5189-5196.
[9] Lo, A.W. (1991) Long Term Memory in Stock Market Prices. Econometrica, 59, 1279-1313.
[10] Joyeux, R. and Granger, C.W.J. (1980) An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time-Series Analysis, 1, 15-29.
[11] Hosking, J.R.M. (1981) Fractional Differencing. Biometrika, 68, 165-176.
[12] Bailliea, R.T., Bollerslev, T. and Mikkelsenc, H.O. (1996) Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 3-30.
[13] Tang T.L. and Shieh, S.J. (2006) Long Memory in Stock Index Future Markets: A Value-at-Risk Approach. Physica A: Statistical Mechanics and its Applications, 366, 437-448.
[14] Cheong, W.C. (2008) Volatility in Malaysian Stock Market: An Empirical Study Using Fractionally Integrated Approach. American Journal of Applied Sciences, 5, 683-688.
[15] Lambert, P. and Laurent, S. (2001) Modelling Financial Time Series Using GARCH-Type Models and a Skewed Student Density. Université de Liège, Mimeo.
[16] Akaike, H. (1974) A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19, 716-723.
[17] Awartani, B. and Corradi, V. (2005) Predicting the Volatility of S&P-500 Stock Index via GARCH Models: The Role of Asymmetries. International Journal of Forecasting, 21, 167-183.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.